Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations14189
Missing cells82812
Missing cells (%)29.2%
Duplicate rows5
Duplicate rows (%)< 0.1%
Total size in memory2.2 MiB
Average record size in memory160.0 B

Variable types

Text2
Numeric17
Categorical1

Alerts

Dataset has 5 (< 0.1%) duplicate rowsDuplicates
espesor_permeable_[m] is highly overall correlated with espesor_total_[m] and 7 other fieldsHigh correlation
espesor_total_[m] is highly overall correlated with espesor_permeable_[m]High correlation
gp_[mmm3] is highly overall correlated with lp_[mm3] and 2 other fieldsHigh correlation
lithology is highly overall correlated with espesor_permeable_[m] and 6 other fieldsHigh correlation
lp_[mm3] is highly overall correlated with gp_[mmm3] and 2 other fieldsHigh correlation
np_[mm3] is highly overall correlated with gp_[mmm3] and 2 other fieldsHigh correlation
phie is highly overall correlated with espesor_permeable_[m] and 6 other fieldsHigh correlation
regiones_de_pvt is highly overall correlated with wi_[mm3]High correlation
res_deep is highly overall correlated with espesor_permeable_[m] and 6 other fieldsHigh correlation
res_shallow is highly overall correlated with espesor_permeable_[m] and 6 other fieldsHigh correlation
sped is highly overall correlated with espesor_permeable_[m] and 6 other fieldsHigh correlation
sw is highly overall correlated with espesor_permeable_[m] and 6 other fieldsHigh correlation
vcl is highly overall correlated with espesor_permeable_[m] and 6 other fieldsHigh correlation
wi_[mm3] is highly overall correlated with gp_[mmm3] and 3 other fieldsHigh correlation
x is highly overall correlated with yHigh correlation
y is highly overall correlated with xHigh correlation
lithology has 2437 (17.2%) missing valuesMissing
res_deep has 901 (6.3%) missing valuesMissing
res_shallow has 3293 (23.2%) missing valuesMissing
vcl has 1799 (12.7%) missing valuesMissing
phie has 5077 (35.8%) missing valuesMissing
sw has 5077 (35.8%) missing valuesMissing
np_[mm3] has 12036 (84.8%) missing valuesMissing
gp_[mmm3] has 12036 (84.8%) missing valuesMissing
lp_[mm3] has 12036 (84.8%) missing valuesMissing
wi_[mm3] has 12036 (84.8%) missing valuesMissing
espesor_permeable_[m] has 4892 (34.5%) missing valuesMissing
eini_int has 11059 (77.9%) missing valuesMissing
res_deep is highly skewed (γ1 = 26.20481722)Skewed
lithology has 278 (2.0%) zerosZeros
np_[mm3] has 778 (5.5%) zerosZeros
gp_[mmm3] has 578 (4.1%) zerosZeros
lp_[mm3] has 627 (4.4%) zerosZeros
wi_[mm3] has 1408 (9.9%) zerosZeros
espesor_permeable_[m] has 1202 (8.5%) zerosZeros

Reproduction

Analysis started2024-08-20 15:57:39.142048
Analysis finished2024-08-20 15:57:55.770048
Duration16.63 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Distinct164
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size111.0 KiB
2024-08-20T17:57:55.882049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.7214039
Min length5

Characters and Unicode

Total characters109559
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPLM-29
2nd rowPLM-29
3rd rowPLM-29
4th rowPLM-29
5th rowPLM-29
ValueCountFrequency (%)
plms-807 113
 
0.8%
plm-848 113
 
0.8%
plms-888 113
 
0.8%
plms-10 112
 
0.8%
plms-818 112
 
0.8%
plms-816 112
 
0.8%
plms-893 111
 
0.8%
plms-887 111
 
0.8%
plms-906 111
 
0.8%
plms-22 111
 
0.8%
Other values (154) 13070
92.1%
2024-08-20T17:57:56.074049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 14189
13.0%
L 14189
13.0%
M 14189
13.0%
- 14189
13.0%
S 12709
11.6%
8 11100
10.1%
9 6000
5.5%
1 3426
 
3.1%
0 3291
 
3.0%
2 3286
 
3.0%
Other values (5) 12991
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 14189
13.0%
L 14189
13.0%
M 14189
13.0%
- 14189
13.0%
S 12709
11.6%
8 11100
10.1%
9 6000
5.5%
1 3426
 
3.1%
0 3291
 
3.0%
2 3286
 
3.0%
Other values (5) 12991
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 14189
13.0%
L 14189
13.0%
M 14189
13.0%
- 14189
13.0%
S 12709
11.6%
8 11100
10.1%
9 6000
5.5%
1 3426
 
3.1%
0 3291
 
3.0%
2 3286
 
3.0%
Other values (5) 12991
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 14189
13.0%
L 14189
13.0%
M 14189
13.0%
- 14189
13.0%
S 12709
11.6%
8 11100
10.1%
9 6000
5.5%
1 3426
 
3.1%
0 3291
 
3.0%
2 3286
 
3.0%
Other values (5) 12991
11.9%

x
Real number (ℝ)

HIGH CORRELATION 

Distinct219
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2543743.4
Minimum2541214.1
Maximum2547025.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.0 KiB
2024-08-20T17:57:56.147049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2541214.1
5-th percentile2541550.2
Q12542663
median2543674.6
Q32544753.3
95-th percentile2546263
Maximum2547025.8
Range5811.68
Interquartile range (IQR)2090.24

Descriptive statistics

Standard deviation1386.0798
Coefficient of variation (CV)0.00054489764
Kurtosis-0.65975141
Mean2543743.4
Median Absolute Deviation (MAD)1043.04
Skewness0.25215712
Sum3.6093175 × 1010
Variance1921217.2
MonotonicityNot monotonic
2024-08-20T17:57:56.216049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2542663.04 195
 
1.4%
2544211.2 187
 
1.3%
2541370.24 177
 
1.2%
2543165.44 175
 
1.2%
2543896.8 112
 
0.8%
2542840.8 112
 
0.8%
2543684.32 111
 
0.8%
2542556.96 111
 
0.8%
2544201.76 111
 
0.8%
2542947.2 111
 
0.8%
Other values (209) 12787
90.1%
ValueCountFrequency (%)
2541214.08 92
0.6%
2541214.08 9
 
0.1%
2541308 100
0.7%
2541370.24 8
 
0.1%
2541370.24 177
1.2%
2541386.72 70
 
0.5%
2541386.72 4
 
< 0.1%
2541410.56 90
0.6%
2541476.32 101
0.7%
2541550.24 8
 
0.1%
ValueCountFrequency (%)
2547025.76 85
0.6%
2546968.32 46
0.3%
2546838.08 58
0.4%
2546750.56 60
0.4%
2546750.56 2
 
< 0.1%
2546711.52 5
 
< 0.1%
2546711.52 91
0.6%
2546557.76 56
0.4%
2546519.04 98
0.7%
2546324.32 87
0.6%

y
Real number (ℝ)

HIGH CORRELATION 

Distinct202
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4918587.4
Minimum4916009.9
Maximum4920772.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.0 KiB
2024-08-20T17:57:56.288049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum4916009.9
5-th percentile4916464.6
Q14917680.6
median4918831.4
Q34919593
95-th percentile4920196.5
Maximum4920772.5
Range4762.56
Interquartile range (IQR)1912.32

Descriptive statistics

Standard deviation1197.2909
Coefficient of variation (CV)0.0002434217
Kurtosis-0.90393543
Mean4918587.4
Median Absolute Deviation (MAD)867.52
Skewness-0.37410797
Sum6.9789837 × 1010
Variance1433505.5
MonotonicityNot monotonic
2024-08-20T17:57:56.362049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4919132.16 187
 
1.3%
4919050.56 113
 
0.8%
4917680.64 112
 
0.8%
4920196.48 112
 
0.8%
4919877.12 111
 
0.8%
4919937.92 111
 
0.8%
4919512 111
 
0.8%
4920696.64 111
 
0.8%
4919518.08 111
 
0.8%
4918307.84 111
 
0.8%
Other values (192) 12999
91.6%
ValueCountFrequency (%)
4916009.92 55
0.4%
4916109.12 61
0.4%
4916248.96 2
 
< 0.1%
4916248.96 81
0.6%
4916258.56 86
0.6%
4916262.08 62
0.4%
4916270.4 70
0.5%
4916282.56 60
0.4%
4916336 98
0.7%
4916363.52 46
0.3%
ValueCountFrequency (%)
4920772.48 105
0.7%
4920772.48 8
 
0.1%
4920696.64 111
0.8%
4920557.12 4
 
< 0.1%
4920557.12 82
0.6%
4920445.12 78
0.5%
4920407.36 105
0.7%
4920391.04 35
 
0.2%
4920274.56 63
0.4%
4920228.48 53
0.4%

capa
Text

Distinct140
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size111.0 KiB
2024-08-20T17:57:56.491049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.474593
Min length2

Characters and Unicode

Total characters49301
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowB-3
2nd rowB-5
3rd rowB-6
4th rowB-7
5th rowB-7C
ValueCountFrequency (%)
j-1bb 162
 
1.1%
j-1bc 162
 
1.1%
h-2 161
 
1.1%
g-6ab 161
 
1.1%
h-7 159
 
1.1%
g-6 158
 
1.1%
h-4 157
 
1.1%
h-6 156
 
1.1%
g-3 156
 
1.1%
j-7 155
 
1.1%
Other values (130) 12602
88.8%
2024-08-20T17:57:56.680049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 14187
28.8%
B 3333
 
6.8%
A 3008
 
6.1%
1 2275
 
4.6%
J 2110
 
4.3%
4 1878
 
3.8%
6 1865
 
3.8%
2 1807
 
3.7%
E 1756
 
3.6%
I 1576
 
3.2%
Other values (16) 15506
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49301
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 14187
28.8%
B 3333
 
6.8%
A 3008
 
6.1%
1 2275
 
4.6%
J 2110
 
4.3%
4 1878
 
3.8%
6 1865
 
3.8%
2 1807
 
3.7%
E 1756
 
3.6%
I 1576
 
3.2%
Other values (16) 15506
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49301
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 14187
28.8%
B 3333
 
6.8%
A 3008
 
6.1%
1 2275
 
4.6%
J 2110
 
4.3%
4 1878
 
3.8%
6 1865
 
3.8%
2 1807
 
3.7%
E 1756
 
3.6%
I 1576
 
3.2%
Other values (16) 15506
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49301
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 14187
28.8%
B 3333
 
6.8%
A 3008
 
6.1%
1 2275
 
4.6%
J 2110
 
4.3%
4 1878
 
3.8%
6 1865
 
3.8%
2 1807
 
3.7%
E 1756
 
3.6%
I 1576
 
3.2%
Other values (16) 15506
31.5%

depth
Real number (ℝ)

Distinct7678
Distinct (%)54.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2103.1428
Minimum1374.65
Maximum2687.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.0 KiB
2024-08-20T17:57:56.750049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1374.65
5-th percentile1631.12
Q11859
median2139.2
Q32332.25
95-th percentile2522.63
Maximum2687.75
Range1313.1
Interquartile range (IQR)473.25

Descriptive statistics

Standard deviation284.11716
Coefficient of variation (CV)0.13509171
Kurtosis-0.99155869
Mean2103.1428
Median Absolute Deviation (MAD)231
Skewness-0.19845606
Sum29841493
Variance80722.562
MonotonicityNot monotonic
2024-08-20T17:57:56.822049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2355.5 9
 
0.1%
2194.25 8
 
0.1%
2291.75 8
 
0.1%
2311.55 8
 
0.1%
2454.95 7
 
< 0.1%
2228 7
 
< 0.1%
2179.4 7
 
< 0.1%
2272.1 7
 
< 0.1%
2219.75 7
 
< 0.1%
2366.3 7
 
< 0.1%
Other values (7668) 14114
99.5%
ValueCountFrequency (%)
1374.65 1
< 0.1%
1375.55 1
< 0.1%
1376.3 1
< 0.1%
1387.1 1
< 0.1%
1392.95 1
< 0.1%
1407.05 1
< 0.1%
1413.2 1
< 0.1%
1413.2 1
< 0.1%
1414.4 1
< 0.1%
1417.1 1
< 0.1%
ValueCountFrequency (%)
2687.75 1
< 0.1%
2685.5 1
< 0.1%
2685.35 1
< 0.1%
2685.2 1
< 0.1%
2679.05 1
< 0.1%
2676.95 1
< 0.1%
2673.65 1
< 0.1%
2673.35 1
< 0.1%
2673.05 1
< 0.1%
2670.5 1
< 0.1%

lithology
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct971
Distinct (%)8.3%
Missing2437
Missing (%)17.2%
Infinite0
Infinite (%)0.0%
Mean0.46518884
Minimum-1
Maximum1
Zeros278
Zeros (%)2.0%
Negative2692
Negative (%)19.0%
Memory size111.0 KiB
2024-08-20T17:57:56.891049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.75
Q10
median0.64705882
Q31
95-th percentile1
Maximum1
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.60248676
Coefficient of variation (CV)1.2951445
Kurtosis-0.42727823
Mean0.46518884
Median Absolute Deviation (MAD)0.35294118
Skewness-0.82774573
Sum5466.8993
Variance0.36299029
MonotonicityNot monotonic
2024-08-20T17:57:56.965049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5133
36.2%
-1 467
 
3.3%
0 278
 
2.0%
0.3333333333 173
 
1.2%
0.5 143
 
1.0%
-0.3333333333 128
 
0.9%
0.2 121
 
0.9%
-0.2 109
 
0.8%
0.6 99
 
0.7%
0.1111111111 85
 
0.6%
Other values (961) 5016
35.4%
(Missing) 2437
17.2%
ValueCountFrequency (%)
-1 467
3.3%
-1 1
 
< 0.1%
-0.9692307692 1
 
< 0.1%
-0.95 1
 
< 0.1%
-0.9428571429 1
 
< 0.1%
-0.935483871 1
 
< 0.1%
-0.9285714286 1
 
< 0.1%
-0.92 1
 
< 0.1%
-0.9130434783 2
 
< 0.1%
-0.9047619048 1
 
< 0.1%
ValueCountFrequency (%)
1 5133
36.2%
1 1
 
< 0.1%
1 1
 
< 0.1%
1 1
 
< 0.1%
0.9714285714 1
 
< 0.1%
0.935483871 1
 
< 0.1%
0.9333333333 1
 
< 0.1%
0.9333333333 1
 
< 0.1%
0.9285714286 1
 
< 0.1%
0.9259259259 1
 
< 0.1%

sped
Real number (ℝ)

HIGH CORRELATION 

Distinct14051
Distinct (%)> 99.9%
Missing132
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean-8.1092707
Minimum-785.7285
Maximum21.940057
Zeros0
Zeros (%)0.0%
Negative13595
Negative (%)95.8%
Memory size111.0 KiB
2024-08-20T17:57:57.040049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-785.7285
5-th percentile-26.085656
Q1-10.31086
median-4.2479024
Q3-2.0064
95-th percentile-0.57343467
Maximum21.940057
Range807.66856
Interquartile range (IQR)8.30446

Descriptive statistics

Standard deviation16.817876
Coefficient of variation (CV)-2.0739074
Kurtosis692.3625
Mean-8.1092707
Median Absolute Deviation (MAD)2.8476024
Skewness-18.379829
Sum-113992.02
Variance282.84097
MonotonicityNot monotonic
2024-08-20T17:57:57.115049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-17.63339478 2
 
< 0.1%
-785.7285 2
 
< 0.1%
-12.28191125 2
 
< 0.1%
-10.21298333 2
 
< 0.1%
-21.37065895 2
 
< 0.1%
-14.59964444 2
 
< 0.1%
-13.44631077 1
 
< 0.1%
-1.299676842 1
 
< 0.1%
-9.21284 1
 
< 0.1%
-1.693544286 1
 
< 0.1%
Other values (14041) 14041
99.0%
(Missing) 132
 
0.9%
ValueCountFrequency (%)
-785.7285 2
< 0.1%
-282.137264 1
< 0.1%
-208.7025243 1
< 0.1%
-208.5310769 1
< 0.1%
-207.1882941 1
< 0.1%
-206.0252629 1
< 0.1%
-204.1034178 1
< 0.1%
-197.9323042 1
< 0.1%
-196.4003109 1
< 0.1%
-196.2252872 1
< 0.1%
ValueCountFrequency (%)
21.9400568 1
< 0.1%
21.6633366 1
< 0.1%
21.32139529 1
< 0.1%
21.2389125 1
< 0.1%
21.04238667 1
< 0.1%
20.97993571 1
< 0.1%
20.79678516 1
< 0.1%
20.61651167 1
< 0.1%
20.56672593 1
< 0.1%
20.3802504 1
< 0.1%

res_deep
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct13283
Distinct (%)> 99.9%
Missing901
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean12.334003
Minimum0.8706
Maximum1566.0912
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.0 KiB
2024-08-20T17:57:57.190049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.8706
5-th percentile1.6859359
Q12.5924808
median4.7977532
Q313.13656
95-th percentile42.143763
Maximum1566.0912
Range1565.2206
Interquartile range (IQR)10.544079

Descriptive statistics

Standard deviation37.657164
Coefficient of variation (CV)3.0531177
Kurtosis882.33523
Mean12.334003
Median Absolute Deviation (MAD)2.8251459
Skewness26.204817
Sum163894.24
Variance1418.062
MonotonicityNot monotonic
2024-08-20T17:57:57.263049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.75414733 2
 
< 0.1%
32.4712975 2
 
< 0.1%
51.6571 2
 
< 0.1%
457.8258513 2
 
< 0.1%
15.00199556 2
 
< 0.1%
3.392162857 1
 
< 0.1%
2.180030667 1
 
< 0.1%
10.20222455 1
 
< 0.1%
3.254842 1
 
< 0.1%
5.110182222 1
 
< 0.1%
Other values (13273) 13273
93.5%
(Missing) 901
 
6.3%
ValueCountFrequency (%)
0.8706 1
< 0.1%
1.094939474 1
< 0.1%
1.155898667 1
< 0.1%
1.225549231 1
< 0.1%
1.242995111 1
< 0.1%
1.250744 1
< 0.1%
1.2515 1
< 0.1%
1.256910909 1
< 0.1%
1.268825455 1
< 0.1%
1.276010909 1
< 0.1%
ValueCountFrequency (%)
1566.091226 1
< 0.1%
1559.771464 1
< 0.1%
1363.483324 1
< 0.1%
1240.69505 1
< 0.1%
1199.129614 1
< 0.1%
1162.044366 1
< 0.1%
988.4321477 1
< 0.1%
966.395065 1
< 0.1%
717.1507874 1
< 0.1%
692.9382757 1
< 0.1%

res_shallow
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10892
Distinct (%)> 99.9%
Missing3293
Missing (%)23.2%
Infinite0
Infinite (%)0.0%
Mean34.309141
Minimum0.93852171
Maximum5733.8038
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.0 KiB
2024-08-20T17:57:57.336049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.93852171
5-th percentile1.84524
Q12.8979569
median5.6226207
Q317.430464
95-th percentile55.65966
Maximum5733.8038
Range5732.8653
Interquartile range (IQR)14.532507

Descriptive statistics

Standard deviation211.47905
Coefficient of variation (CV)6.1639273
Kurtosis213.52093
Mean34.309141
Median Absolute Deviation (MAD)3.5243494
Skewness13.168674
Sum373832.4
Variance44723.388
MonotonicityNot monotonic
2024-08-20T17:57:57.405049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.7559825 2
 
< 0.1%
29.60232737 2
 
< 0.1%
26.00118783 2
 
< 0.1%
2245.4566 2
 
< 0.1%
14.13638364 1
 
< 0.1%
2.387906154 1
 
< 0.1%
2.283443333 1
 
< 0.1%
60.833515 1
 
< 0.1%
96.92751692 1
 
< 0.1%
2.224526667 1
 
< 0.1%
Other values (10882) 10882
76.7%
(Missing) 3293
 
23.2%
ValueCountFrequency (%)
0.9385217105 1
< 0.1%
1.273384615 1
< 0.1%
1.301001667 1
< 0.1%
1.301538044 1
< 0.1%
1.3125712 1
< 0.1%
1.333364 1
< 0.1%
1.362849091 1
< 0.1%
1.363211429 1
< 0.1%
1.401561538 1
< 0.1%
1.402 1
< 0.1%
ValueCountFrequency (%)
5733.80384 1
< 0.1%
5349.713293 1
< 0.1%
4097.411536 1
< 0.1%
3839.863338 1
< 0.1%
3835.659773 1
< 0.1%
3809.762925 1
< 0.1%
3457.907822 1
< 0.1%
3456.477732 1
< 0.1%
3435.951046 1
< 0.1%
3152.2507 1
< 0.1%

vcl
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10859
Distinct (%)87.6%
Missing1799
Missing (%)12.7%
Infinite0
Infinite (%)0.0%
Mean0.66974149
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size111.0 KiB
2024-08-20T17:57:57.477049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.17972507
Q10.41902579
median0.70342264
Q30.96911445
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.55008865

Descriptive statistics

Standard deviation0.28842544
Coefficient of variation (CV)0.43065189
Kurtosis-1.2555722
Mean0.66974149
Median Absolute Deviation (MAD)0.2717839
Skewness-0.34459026
Sum8298.0971
Variance0.083189234
MonotonicityNot monotonic
2024-08-20T17:57:57.549049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1526
 
10.8%
0.4917577778 2
 
< 0.1%
0.49321 2
 
< 0.1%
0.3157536842 2
 
< 0.1%
0.4169017391 2
 
< 0.1%
0.99988 2
 
< 0.1%
0.536556 2
 
< 0.1%
0.180964 1
 
< 0.1%
0.9932882353 1
 
< 0.1%
0.44917 1
 
< 0.1%
Other values (10849) 10849
76.5%
(Missing) 1799
 
12.7%
ValueCountFrequency (%)
0 1
< 0.1%
0.02612 1
< 0.1%
0.03435 1
< 0.1%
0.0487944 1
< 0.1%
0.04928285714 1
< 0.1%
0.049679 1
< 0.1%
0.05455333333 1
< 0.1%
0.05851963636 1
< 0.1%
0.05854184615 1
< 0.1%
0.05922153846 1
< 0.1%
ValueCountFrequency (%)
1 1526
10.8%
1 1
 
< 0.1%
1 1
 
< 0.1%
0.9999963636 1
 
< 0.1%
0.9999955556 1
 
< 0.1%
0.999995 1
 
< 0.1%
0.999992 1
 
< 0.1%
0.9999907692 1
 
< 0.1%
0.9999816667 1
 
< 0.1%
0.9999796364 1
 
< 0.1%

phie
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5846
Distinct (%)64.2%
Missing5077
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean0.046885373
Minimum0.0001
Maximum0.397405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.0 KiB
2024-08-20T17:57:57.618049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.0001
5-th percentile0.0001
Q10.0001
median0.030202222
Q30.08593662
95-th percentile0.13711569
Maximum0.397405
Range0.397305
Interquartile range (IQR)0.08583662

Descriptive statistics

Standard deviation0.051591768
Coefficient of variation (CV)1.1003809
Kurtosis1.4104918
Mean0.046885373
Median Absolute Deviation (MAD)0.030102222
Skewness1.0549413
Sum427.21952
Variance0.0026617105
MonotonicityNot monotonic
2024-08-20T17:57:57.944049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0001 2971
20.9%
0.0001 292
 
2.1%
0.08494666667 2
 
< 0.1%
0.064096 2
 
< 0.1%
0.06692 2
 
< 0.1%
0.1079715789 2
 
< 0.1%
0.07811391304 2
 
< 0.1%
0.1624212174 1
 
< 0.1%
0.09754165714 1
 
< 0.1%
0.05321104 1
 
< 0.1%
Other values (5836) 5836
41.1%
(Missing) 5077
35.8%
ValueCountFrequency (%)
0.0001 2971
20.9%
0.0001 292
 
2.1%
0.0001 1
 
< 0.1%
0.0001373333333 1
 
< 0.1%
0.0001875 1
 
< 0.1%
0.0002336842105 1
 
< 0.1%
0.00029216 1
 
< 0.1%
0.0003084210526 1
 
< 0.1%
0.0003293333333 1
 
< 0.1%
0.00038 1
 
< 0.1%
ValueCountFrequency (%)
0.397405 1
< 0.1%
0.3672666667 1
< 0.1%
0.3555270588 1
< 0.1%
0.3531336364 1
< 0.1%
0.349352 1
< 0.1%
0.3489422222 1
< 0.1%
0.3465908333 1
< 0.1%
0.333465 1
< 0.1%
0.3298813333 1
< 0.1%
0.3288381818 1
< 0.1%

sw
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4455
Distinct (%)48.9%
Missing5077
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean0.90269156
Minimum0.1017475
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.0 KiB
2024-08-20T17:57:58.019049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.1017475
5-th percentile0.58551039
Q10.83464332
median1
Q31
95-th percentile1
Maximum1
Range0.8982525
Interquartile range (IQR)0.16535668

Descriptive statistics

Standard deviation0.14662693
Coefficient of variation (CV)0.16243304
Kurtosis1.5952447
Mean0.90269156
Median Absolute Deviation (MAD)0
Skewness-1.5374832
Sum8225.3255
Variance0.021499457
MonotonicityNot monotonic
2024-08-20T17:57:58.090049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4653
32.8%
0.6383821053 2
 
< 0.1%
0.76092125 2
 
< 0.1%
0.8172153333 2
 
< 0.1%
0.6201034783 2
 
< 0.1%
0.8221266667 2
 
< 0.1%
0.8532707692 1
 
< 0.1%
0.7660626667 1
 
< 0.1%
0.7784925 1
 
< 0.1%
0.9962977778 1
 
< 0.1%
Other values (4445) 4445
31.3%
(Missing) 5077
35.8%
ValueCountFrequency (%)
0.1017475 1
< 0.1%
0.1466969231 1
< 0.1%
0.1948442857 1
< 0.1%
0.2236833333 1
< 0.1%
0.259284 1
< 0.1%
0.2682176471 1
< 0.1%
0.2797944444 1
< 0.1%
0.292086 1
< 0.1%
0.29594 1
< 0.1%
0.3095009091 1
< 0.1%
ValueCountFrequency (%)
1 4653
32.8%
0.99998 1
 
< 0.1%
0.9999666667 1
 
< 0.1%
0.99996 1
 
< 0.1%
0.9999494737 1
 
< 0.1%
0.9999422222 1
 
< 0.1%
0.9999371429 1
 
< 0.1%
0.99992 1
 
< 0.1%
0.9999031579 1
 
< 0.1%
0.99989 1
 
< 0.1%

np_[mm3]
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1210
Distinct (%)56.2%
Missing12036
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean2.4290065
Minimum-0.001
Maximum72.122
Zeros778
Zeros (%)5.5%
Negative1
Negative (%)< 0.1%
Memory size111.0 KiB
2024-08-20T17:57:58.154049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-0.001
5-th percentile0
Q10
median0.669
Q32.392
95-th percentile11.1916
Maximum72.122
Range72.123
Interquartile range (IQR)2.392

Descriptive statistics

Standard deviation5.3322642
Coefficient of variation (CV)2.1952449
Kurtosis42.051311
Mean2.4290065
Median Absolute Deviation (MAD)0.669
Skewness5.3460551
Sum5229.651
Variance28.433042
MonotonicityNot monotonic
2024-08-20T17:57:58.226048image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 778
 
5.5%
0.524 5
 
< 0.1%
0.101 4
 
< 0.1%
0.478 4
 
< 0.1%
0.862 4
 
< 0.1%
2.873 4
 
< 0.1%
0.282 3
 
< 0.1%
0.646 3
 
< 0.1%
0.962 3
 
< 0.1%
3.486 3
 
< 0.1%
Other values (1200) 1342
 
9.5%
(Missing) 12036
84.8%
ValueCountFrequency (%)
-0.001 1
 
< 0.1%
0 778
5.5%
0.003 1
 
< 0.1%
0.007 1
 
< 0.1%
0.009 1
 
< 0.1%
0.012 3
 
< 0.1%
0.014 1
 
< 0.1%
0.018 1
 
< 0.1%
0.019 2
 
< 0.1%
0.021 1
 
< 0.1%
ValueCountFrequency (%)
72.122 1
< 0.1%
64.027 1
< 0.1%
55.005 1
< 0.1%
53.43 1
< 0.1%
47.329 1
< 0.1%
46.44 1
< 0.1%
40.059 1
< 0.1%
39.257 1
< 0.1%
35.545 1
< 0.1%
34.626 1
< 0.1%

gp_[mmm3]
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct123
Distinct (%)5.7%
Missing12036
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean0.016645611
Minimum0
Maximum0.256
Zeros578
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size111.0 KiB
2024-08-20T17:57:58.300049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.009
Q30.022
95-th percentile0.0584
Maximum0.256
Range0.256
Interquartile range (IQR)0.022

Descriptive statistics

Standard deviation0.025145216
Coefficient of variation (CV)1.5106214
Kurtosis20.91596
Mean0.016645611
Median Absolute Deviation (MAD)0.009
Skewness3.7604471
Sum35.838
Variance0.00063228188
MonotonicityNot monotonic
2024-08-20T17:57:58.371049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 578
 
4.1%
0.005 66
 
0.5%
0.007 65
 
0.5%
0.012 62
 
0.4%
0.008 61
 
0.4%
0.003 60
 
0.4%
0.009 60
 
0.4%
0.004 58
 
0.4%
0.01 57
 
0.4%
0.006 56
 
0.4%
Other values (113) 1030
 
7.3%
(Missing) 12036
84.8%
ValueCountFrequency (%)
0 578
4.1%
0.001 44
 
0.3%
0.002 46
 
0.3%
0.003 60
 
0.4%
0.004 58
 
0.4%
0.005 66
 
0.5%
0.006 56
 
0.4%
0.007 65
 
0.5%
0.008 61
 
0.4%
0.009 60
 
0.4%
ValueCountFrequency (%)
0.256 1
< 0.1%
0.232 1
< 0.1%
0.231 1
< 0.1%
0.219 1
< 0.1%
0.208 1
< 0.1%
0.193 1
< 0.1%
0.187 1
< 0.1%
0.18 1
< 0.1%
0.178 1
< 0.1%
0.177 1
< 0.1%

lp_[mm3]
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1368
Distinct (%)63.5%
Missing12036
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean4.7559275
Minimum-0.001
Maximum150.678
Zeros627
Zeros (%)4.4%
Negative1
Negative (%)< 0.1%
Memory size111.0 KiB
2024-08-20T17:57:58.439049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum-0.001
5-th percentile0
Q10
median1.081
Q34.217
95-th percentile22.322
Maximum150.678
Range150.679
Interquartile range (IQR)4.217

Descriptive statistics

Standard deviation10.985007
Coefficient of variation (CV)2.3097507
Kurtosis49.716867
Mean4.7559275
Median Absolute Deviation (MAD)1.081
Skewness5.8076845
Sum10239.512
Variance120.67038
MonotonicityNot monotonic
2024-08-20T17:57:58.511049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 627
 
4.4%
0.524 5
 
< 0.1%
2.873 4
 
< 0.1%
0.862 4
 
< 0.1%
0.478 4
 
< 0.1%
1.786 3
 
< 0.1%
0.375 3
 
< 0.1%
1.581 3
 
< 0.1%
0.585 3
 
< 0.1%
0.501 3
 
< 0.1%
Other values (1358) 1494
 
10.5%
(Missing) 12036
84.8%
ValueCountFrequency (%)
-0.001 1
 
< 0.1%
0 627
4.4%
0.003 1
 
< 0.1%
0.007 1
 
< 0.1%
0.009 1
 
< 0.1%
0.012 3
 
< 0.1%
0.014 1
 
< 0.1%
0.018 1
 
< 0.1%
0.019 2
 
< 0.1%
0.021 1
 
< 0.1%
ValueCountFrequency (%)
150.678 1
< 0.1%
144.31 1
< 0.1%
119.423 1
< 0.1%
115.154 1
< 0.1%
103.648 1
< 0.1%
95.773 1
< 0.1%
90.109 1
< 0.1%
83.248 1
< 0.1%
72.122 1
< 0.1%
69.192 1
< 0.1%

wi_[mm3]
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct742
Distinct (%)34.5%
Missing12036
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean30.197573
Minimum0
Maximum612.716
Zeros1408
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size111.0 KiB
2024-08-20T17:57:58.581048image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q326.355
95-th percentile178.7222
Maximum612.716
Range612.716
Interquartile range (IQR)26.355

Descriptive statistics

Standard deviation66.995419
Coefficient of variation (CV)2.2185697
Kurtosis14.842039
Mean30.197573
Median Absolute Deviation (MAD)0
Skewness3.3456439
Sum65015.374
Variance4488.3861
MonotonicityNot monotonic
2024-08-20T17:57:58.650049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1408
 
9.9%
9.178 2
 
< 0.1%
20.461 2
 
< 0.1%
0.132 2
 
< 0.1%
12.556 2
 
< 0.1%
165.349 1
 
< 0.1%
69.444 1
 
< 0.1%
145.611 1
 
< 0.1%
72.815 1
 
< 0.1%
124.234 1
 
< 0.1%
Other values (732) 732
 
5.2%
(Missing) 12036
84.8%
ValueCountFrequency (%)
0 1408
9.9%
0.088 1
 
< 0.1%
0.132 2
 
< 0.1%
0.236 1
 
< 0.1%
0.241 1
 
< 0.1%
0.344 1
 
< 0.1%
0.35 1
 
< 0.1%
0.412 1
 
< 0.1%
0.417 1
 
< 0.1%
0.499 1
 
< 0.1%
ValueCountFrequency (%)
612.716 1
< 0.1%
591.552 1
< 0.1%
549.637 1
< 0.1%
481.849 1
< 0.1%
464.78 1
< 0.1%
459.715 1
< 0.1%
457.709 1
< 0.1%
435.583 1
< 0.1%
419.629 1
< 0.1%
405.837 1
< 0.1%

espesor_permeable_[m]
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct791
Distinct (%)8.5%
Missing4892
Missing (%)34.5%
Infinite0
Infinite (%)0.0%
Mean2.3530354
Minimum0
Maximum12.59
Zeros1202
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size111.0 KiB
2024-08-20T17:57:58.720049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.81
median2.16
Q33.51
95-th percentile5.73
Maximum12.59
Range12.59
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation1.855272
Coefficient of variation (CV)0.78845904
Kurtosis0.63259879
Mean2.3530354
Median Absolute Deviation (MAD)1.35
Skewness0.77998329
Sum21876.17
Variance3.4420343
MonotonicityNot monotonic
2024-08-20T17:57:58.793049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1202
 
8.5%
0.5 55
 
0.4%
1.25 50
 
0.4%
0.75 49
 
0.3%
1.38 47
 
0.3%
0.63 47
 
0.3%
1.13 46
 
0.3%
0.38 45
 
0.3%
0.88 43
 
0.3%
0.25 42
 
0.3%
Other values (781) 7671
54.1%
(Missing) 4892
34.5%
ValueCountFrequency (%)
0 1202
8.5%
0.01 8
 
0.1%
0.02 12
 
0.1%
0.03 11
 
0.1%
0.04 7
 
< 0.1%
0.05 6
 
< 0.1%
0.06 6
 
< 0.1%
0.07 2
 
< 0.1%
0.08 4
 
< 0.1%
0.09 14
 
0.1%
ValueCountFrequency (%)
12.59 1
< 0.1%
12.27 1
< 0.1%
11.67 1
< 0.1%
10.87 1
< 0.1%
10.78 1
< 0.1%
10.73 1
< 0.1%
10.61 1
< 0.1%
10.52 1
< 0.1%
10.34 1
< 0.1%
10.33 1
< 0.1%

espesor_total_[m]
Real number (ℝ)

HIGH CORRELATION 

Distinct929
Distinct (%)6.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.4169171
Minimum0.4
Maximum15.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.0 KiB
2024-08-20T17:57:58.861048image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile2.12
Q13.2
median4.16
Q35.38
95-th percentile7.71
Maximum15.16
Range14.76
Interquartile range (IQR)2.18

Descriptive statistics

Standard deviation1.7520245
Coefficient of variation (CV)0.3966623
Kurtosis1.452836
Mean4.4169171
Median Absolute Deviation (MAD)1.065
Skewness0.8378547
Sum62667.22
Variance3.0695899
MonotonicityNot monotonic
2024-08-20T17:57:58.932049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 116
 
0.8%
0.45 64
 
0.5%
3.42 60
 
0.4%
3.28 58
 
0.4%
3.72 54
 
0.4%
3.99 53
 
0.4%
4.17 51
 
0.4%
4.22 51
 
0.4%
2.99 50
 
0.4%
3.68 50
 
0.4%
Other values (919) 13581
95.7%
ValueCountFrequency (%)
0.4 2
 
< 0.1%
0.45 64
0.5%
0.48 1
 
< 0.1%
0.5 116
0.8%
0.75 1
 
< 0.1%
0.77 2
 
< 0.1%
0.78 1
 
< 0.1%
0.79 1
 
< 0.1%
0.81 1
 
< 0.1%
0.94 1
 
< 0.1%
ValueCountFrequency (%)
15.16 1
< 0.1%
14.92 1
< 0.1%
14.16 1
< 0.1%
14.09 1
< 0.1%
13.85 1
< 0.1%
13.75 1
< 0.1%
13.46 1
< 0.1%
13.39 1
< 0.1%
13.3 1
< 0.1%
13.24 1
< 0.1%

regiones_de_pvt
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7939249
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size111.0 KiB
2024-08-20T17:57:58.993049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q14
median4
Q36
95-th percentile6
Maximum10
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0782546
Coefficient of variation (CV)0.22492104
Kurtosis0.47351585
Mean4.7939249
Median Absolute Deviation (MAD)0
Skewness0.96391643
Sum68021
Variance1.162633
MonotonicityNot monotonic
2024-08-20T17:57:59.048049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 8430
59.4%
6 4648
32.8%
5 633
 
4.5%
8 181
 
1.3%
3 102
 
0.7%
9 90
 
0.6%
7 90
 
0.6%
2 12
 
0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
2 12
 
0.1%
3 102
 
0.7%
4 8430
59.4%
5 633
 
4.5%
6 4648
32.8%
7 90
 
0.6%
8 181
 
1.3%
9 90
 
0.6%
10 3
 
< 0.1%
ValueCountFrequency (%)
10 3
 
< 0.1%
9 90
 
0.6%
8 181
 
1.3%
7 90
 
0.6%
6 4648
32.8%
5 633
 
4.5%
4 8430
59.4%
3 102
 
0.7%
2 12
 
0.1%

eini_int
Categorical

MISSING 

Distinct23
Distinct (%)0.7%
Missing11059
Missing (%)77.9%
Memory size111.0 KiB
NE
1025 
SE
416 
NR
302 
W-C
266 
W-BC
180 
Other values (18)
941 

Length

Max length8
Median length2
Mean length2.7488818
Min length1

Characters and Unicode

Total characters8604
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowO
2nd rowSE
3rd rowNR
4th rowSE
5th rowNR

Common Values

ValueCountFrequency (%)
NE 1025
 
7.2%
SE 416
 
2.9%
NR 302
 
2.1%
W-C 266
 
1.9%
W-BC 180
 
1.3%
W-C-BC 174
 
1.2%
W 162
 
1.1%
OW-C 108
 
0.8%
WO-C 84
 
0.6%
OW 72
 
0.5%
Other values (13) 341
 
2.4%
(Missing) 11059
77.9%

Length

2024-08-20T17:57:59.115049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ne 1025
32.7%
se 416
13.3%
nr 302
 
9.6%
w-c 266
 
8.5%
w-bc 180
 
5.8%
w-c-bc 174
 
5.6%
w 162
 
5.2%
ow-c 108
 
3.5%
wo-c 84
 
2.7%
ow 72
 
2.3%
Other values (13) 341
 
10.9%

Most occurring characters

ValueCountFrequency (%)
E 1441
16.7%
N 1327
15.4%
W 1321
15.4%
- 1290
15.0%
C 1290
15.0%
O 605
7.0%
B 516
 
6.0%
S 416
 
4.8%
R 398
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8604
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1441
16.7%
N 1327
15.4%
W 1321
15.4%
- 1290
15.0%
C 1290
15.0%
O 605
7.0%
B 516
 
6.0%
S 416
 
4.8%
R 398
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8604
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1441
16.7%
N 1327
15.4%
W 1321
15.4%
- 1290
15.0%
C 1290
15.0%
O 605
7.0%
B 516
 
6.0%
S 416
 
4.8%
R 398
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8604
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1441
16.7%
N 1327
15.4%
W 1321
15.4%
- 1290
15.0%
C 1290
15.0%
O 605
7.0%
B 516
 
6.0%
S 416
 
4.8%
R 398
 
4.6%

Interactions

2024-08-20T17:57:54.504049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:39.565049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:40.421049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:41.526049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:42.379049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:43.279049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:44.158049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:45.042049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:46.456049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:47.298049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:48.172049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:49.169049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:50.039049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:50.872049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2024-08-20T17:57:52.567049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:53.636049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:54.561049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2024-08-20T17:57:48.264049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:49.268049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:50.134049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:50.969049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:51.827049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2024-08-20T17:57:39.715049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2024-08-20T17:57:54.827049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2024-08-20T17:57:40.720049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2024-08-20T17:57:53.584049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2024-08-20T17:57:54.450049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2024-08-20T17:57:59.168049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
deptheini_intespesor_permeable_[m]espesor_total_[m]gp_[mmm3]lithologylp_[mm3]np_[mm3]phieregiones_de_pvtres_deepres_shallowspedswvclwi_[mm3]xy
depth1.0000.093-0.116-0.271-0.0630.0060.030-0.023-0.1430.0030.2770.287-0.0940.0660.051-0.0560.028-0.027
eini_int0.0931.0000.0430.0440.1350.0770.0790.2230.0640.1290.0900.0560.0370.0740.0370.1140.1340.127
espesor_permeable_[m]-0.1160.0431.0000.5330.234-0.6550.0820.0960.8520.0560.6750.620-0.551-0.781-0.8130.0440.080-0.036
espesor_total_[m]-0.2710.0440.5331.0000.123-0.3180.0810.0880.3890.0280.2570.244-0.249-0.389-0.3460.0050.066-0.093
gp_[mmm3]-0.0630.1350.2340.1231.000-0.0510.6730.6720.1460.3690.0760.108-0.133-0.175-0.170-0.672-0.060-0.010
lithology0.0060.077-0.655-0.318-0.0511.000-0.000-0.035-0.839-0.023-0.816-0.7990.7450.8070.840-0.071-0.004-0.032
lp_[mm3]0.0300.0790.0820.0810.673-0.0001.0000.7630.0140.3110.0510.081-0.034-0.095-0.043-0.6770.248-0.367
np_[mm3]-0.0230.2230.0960.0880.672-0.0350.7631.0000.0360.2830.0010.038-0.039-0.067-0.041-0.587-0.034-0.066
phie-0.1430.0640.8520.3890.146-0.8390.0140.0361.0000.0300.8440.830-0.679-0.893-0.9410.0890.0340.012
regiones_de_pvt0.0030.1290.0560.0280.369-0.0230.3110.2830.0301.0000.0300.036-0.087-0.053-0.029-0.5220.302-0.139
res_deep0.2770.0900.6750.2570.076-0.8160.0510.0010.8440.0301.0000.983-0.724-0.866-0.872-0.0220.0400.043
res_shallow0.2870.0560.6200.2440.108-0.7990.0810.0380.8300.0360.9831.000-0.709-0.846-0.858-0.0410.0010.053
sped-0.0940.037-0.551-0.249-0.1330.745-0.034-0.039-0.679-0.087-0.724-0.7091.0000.6790.6890.016-0.052-0.028
sw0.0660.074-0.781-0.389-0.1750.807-0.095-0.067-0.893-0.053-0.866-0.8460.6791.0000.8640.017-0.0910.005
vcl0.0510.037-0.813-0.346-0.1700.840-0.043-0.041-0.941-0.029-0.872-0.8580.6890.8641.000-0.050-0.055-0.015
wi_[mm3]-0.0560.1140.0440.005-0.672-0.071-0.677-0.5870.089-0.522-0.022-0.0410.0160.017-0.0501.000-0.2150.146
x0.0280.1340.0800.066-0.060-0.0040.248-0.0340.0340.3020.0400.001-0.052-0.091-0.055-0.2151.000-0.594
y-0.0270.127-0.036-0.093-0.010-0.032-0.367-0.0660.012-0.1390.0430.053-0.0280.005-0.0150.146-0.5941.000

Missing values

2024-08-20T17:57:55.425049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-20T17:57:55.561049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-20T17:57:55.685049image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

identificadorxycapadepthlithologyspedres_deepres_shallowvclphieswnp_[mm3]gp_[mmm3]lp_[mm3]wi_[mm3]espesor_permeable_[m]espesor_total_[m]regiones_de_pvteini_int
0PLM-292541370.2404919097.920B-31392.9500.500-5.6143.392NaNNaNNaNNaNNaNNaNNaNNaN3.6308.2404.000NaN
1PLM-292541370.2404919097.920B-51452.3501.000-1.2782.351NaNNaNNaNNaNNaNNaNNaNNaNNaN3.7604.000NaN
2PLM-292541370.2404919097.920B-61478.000-0.739-17.8766.256NaNNaNNaNNaNNaNNaNNaNNaN6.6607.0004.000NaN
3PLM-292541370.2404919097.920B-71497.8001.000-2.1201.937NaNNaNNaNNaNNaNNaNNaNNaN0.0003.4104.000NaN
4PLM-292541370.2404919097.920B-7C1504.850-0.875-6.7596.060NaNNaNNaNNaNNaNNaNNaNNaN4.4704.7904.000NaN
5PLM-292541370.2404919097.920C-71517.4501.000-1.0091.428NaNNaNNaNNaNNaNNaNNaNNaN0.0003.7604.000NaN
6PLM-292541370.2404919097.920C-81524.6500.400-2.4622.469NaNNaNNaNNaNNaNNaNNaNNaN1.0606.0204.000NaN
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identificadorxycapadepthlithologyspedres_deepres_shallowvclphieswnp_[mm3]gp_[mmm3]lp_[mm3]wi_[mm3]espesor_permeable_[m]espesor_total_[m]regiones_de_pvteini_int
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14181PLMS-9832542327.2004919642.880M-1B2436.500NaN19.533NaNNaN0.713NaNNaNNaNNaNNaNNaN1.4004.9204.000NaN
14182PLMS-9832542327.2004919642.880M-22453.450NaN17.421NaNNaN0.301NaNNaNNaNNaNNaNNaN1.4501.8304.000SE
14183PLMS-9832542327.2004919642.880M-2AB2455.850NaN18.752NaNNaN0.552NaNNaNNaNNaNNaNNaN1.6503.0404.000SE
14184PLMS-9832542327.2004919642.880M-32485.100NaN20.353NaNNaN0.670NaNNaNNaNNaNNaNNaN1.0002.7804.000NaN
14185PLMS-9832542327.2004919642.880M-4A2496.500NaN16.770NaNNaN0.348NaNNaN0.3940.0060.3940.0005.8707.9108.000WRO-C
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14187PLMS-9832542327.2004919642.880M-8A2558.750NaN21.239NaNNaN0.919NaNNaNNaNNaNNaNNaNNaN4.8104.000NaN
14188PLMS-9832542327.2004919642.880SC2474.150NaN15.348NaNNaN0.199NaNNaN1.6350.0001.6350.0004.1004.8504.000NaN

Duplicate rows

Most frequently occurring

identificadorxycapadepthlithologyspedres_deepres_shallowvclphieswnp_[mm3]gp_[mmm3]lp_[mm3]wi_[mm3]espesor_permeable_[m]espesor_total_[m]regiones_de_pvteini_int# duplicates
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2PLMS-92543522.2404918196.480E-91828.8500.200-10.21311.754NaN0.5370.0640.8177.0770.0547.0770.0003.9208.9706.000O-BC2
3PLMS-9522545319.0404918307.520I-1B2155.100NaN-21.37151.65729.6020.3160.1080.6383.4860.0123.4860.0003.7005.6804.000OW2
4PLMS-9522545319.0404918307.520I-22168.900NaN-17.633457.82626.0010.4170.0780.6202.8730.0132.8730.0004.1406.6804.000OW2